McqMate
| 1. |
A 3-input neuron is trained to output a zero when the input is 110 and a one when the input is 111. After generalization, the output will be zero when and only when the input is: |
| A. | 000 or 110 or 011 or 101 |
| B. | 010 or 100 or 110 or 101 |
| C. | 000 or 010 or 110 or 100 |
| D. | 100 or 111 or 101 or 001 |
| Answer» C. 000 or 010 or 110 or 100 | |
| 2. |
A perceptron is: |
| A. | a single layer feed-forward neural network with pre-processing |
| B. | an auto-associative neural network |
| C. | a double layer auto-associative neural network |
| D. | a neural network that contains feedback |
| Answer» A. a single layer feed-forward neural network with pre-processing | |
| 3. |
An auto-associative network is: |
| A. | a neural network that contains no loops |
| B. | a neural network that contains feedback |
| C. | a neural network that has only one loop |
| D. | a single layer feed-forward neural network with pre-processing |
| Answer» B. a neural network that contains feedback | |
| 4. |
A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 5 and 20 respectively. The output will be: |
| A. | 238 |
| B. | 76 |
| C. | 119 |
| D. | 123 |
| Answer» A. 238 | |
| 5. |
Which is true for neural networks? |
| A. | it has set of nodes and connections |
| B. | each node computes it’s weighted input |
| C. | node could be in excited state or non-excited state |
| D. | all of the mentioned |
| Answer» D. all of the mentioned | |
| 6. |
Neuro software is: |
| A. | a software used to analyze neurons |
| B. | it is powerful and easy neural network |
| C. | designed to aid experts in real world |
| D. | it is software used by neuro surgeon |
| Answer» B. it is powerful and easy neural network | |
| 7. |
Why is the XOR problem exceptionally interesting to neural network researchers? |
| A. | because it can be expressed in a way that allows you to use a neural network |
| B. | because it is complex binary operation that cannot be solved using neural networks |
| C. | because it can be solved by a single layer perceptron |
| D. | because it is the simplest linearly inseparable problem that exists. |
| Answer» D. because it is the simplest linearly inseparable problem that exists. | |
| 8. |
What is back propagation? |
| A. | it is another name given to the curvy function in the perceptron |
| B. | it is the transmission of error back through the network to adjust the inputs |
| C. | it is the transmission of error back through the network to allow weights to be adjusted so that the network can learn. |
| D. | none of the mentioned |
| Answer» C. it is the transmission of error back through the network to allow weights to be adjusted so that the network can learn. | |
| 9. |
Why are linearly separable problems of interest of neural network researchers? |
| A. | because they are the only class of problem that network can solve successfully |
| B. | because they are the only class of problem that perceptron can solve successfully |
| C. | because they are the only mathematical functions that are continue |
| D. | because they are the only mathematical functions you can draw |
| Answer» B. because they are the only class of problem that perceptron can solve successfully | |
| 10. |
Which of the following is not the promise of artificial neural network? |
| A. | it can explain result |
| B. | it can survive the failure of some nodes |
| C. | it has inherent parallelism |
| D. | it can handle noise |
| Answer» A. it can explain result | |
| 11. |
A perceptron adds up all the weighted inputs it receives, and if it exceeds a certain value, it outputs a 1, otherwise it just outputs a 0. |
| A. | true |
| B. | false |
| C. | sometimes – it can also output intermediate values as well |
| D. | can’t say |
| Answer» A. true | |
| 12. |
The name for the function in question 16 is |
| A. | step function |
| B. | heaviside function |
| C. | logistic function |
| D. | perceptron function |
| Answer» B. heaviside function | |
| 13. |
Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results. |
| A. | true – this works always, and these multiple perceptrons learn to classify even complex problems. |
| B. | false – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do |
| C. | true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded |
| D. | false – just having a single perceptron is enough |
| Answer» C. true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded | |
| 14. |
The network that involves backward links from output to the input and hidden layers is called as ____. |
| A. | self organizing maps |
| B. | perceptrons |
| C. | recurrent neural network |
| D. | multi layered perceptron |
| Answer» C. recurrent neural network | |
| 15. |
Different learning method does not include: |
| A. | memorization |
| B. | analogy |
| C. | deduction |
| D. | introduction |
| Answer» D. introduction | |
| 16. |
Following are the advantage/s of Decision Trees. Choose that apply. |
| A. | possible scenarios can be added |
| B. | for data including categorical variables with different number of levels, information gain in decision trees are biased in favor of those attributes with more levels |
| C. | worst, best and expected values can be determined for different scenarios |
| D. | use a white box model, if given result is provided by a model |
| Answer» D. use a white box model, if given result is provided by a model | |
| 17. |
Which of the following is the model used for learning? |
| A. | decision trees |
| B. | neural networks |
| C. | propositional and fol rules |
| D. | all of the mentioned |
| Answer» D. all of the mentioned | |
| 18. |
Automated vehicle is an example of ______. |
| A. | supervised learning |
| B. | unsupervised learning |
| C. | active learning |
| D. | reinforcement learning |
| Answer» A. supervised learning | |
| 19. |
Following is an example of active learning: |
| A. | news recommendation system |
| B. | dust cleaning machine |
| C. | automated vehicle |
| D. | none of the mentioned |
| Answer» A. news recommendation system | |
| 20. |
In which of the following learning the teacher returns reward and punishment to learner? |
| A. | active learning |
| B. | reinforcement learning |
| C. | supervised learning |
| D. | unsupervised learning |
| Answer» B. reinforcement learning | |
| 21. |
Decision trees are appropriate for the problems where: |
| A. | attributes are both numeric and nominal |
| B. | target function takes on a discrete number of values. |
| C. | data may have errors |
| D. | all of the mentioned |
| Answer» D. all of the mentioned | |
| 22. |
Which of the following is not an application of learning? |
| A. | data mining |
| B. | www |
| C. | speech recognition |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |
| 23. |
Which of the following is the component of learning system? |
| A. | goal |
| B. | model |
| C. | learning rules |
| D. | all of the mentioned |
| Answer» D. all of the mentioned | |
| 24. |
Following is also called as exploratory learning: |
| A. | supervised learning |
| B. | active learning |
| C. | unsupervised learning |
| D. | reinforcement learning |
| Answer» C. unsupervised learning | |
| 25. |
What will take place as the agent observes its interactions with the world? |
| A. | learning |
| B. | hearing |
| C. | perceiving |
| D. | speech |
| Answer» A. learning | |
| 26. |
Which modifies the performance element so that it makes better decision? |
| A. | performance element |
| B. | changing element |
| C. | learning element |
| D. | none of the mentioned |
| Answer» C. learning element | |
| 27. |
How many things are concerned in design of a learning element? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» C. 3 | |
| 28. |
What is used in determining the nature of the learning problem? |
| A. | environment |
| B. | feedback |
| C. | problem |
| D. | all of the mentioned |
| Answer» B. feedback | |
| 29. |
How many types are available in machine learning? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» C. 3 | |
| 30. |
Which is used for utility functions in game playing algorithm? |
| A. | linear polynomial |
| B. | weighted polynomial |
| C. | polynomial |
| D. | linear weighted polynomial |
| Answer» D. linear weighted polynomial | |
| 31. |
Which is used to choose among multiple consistent hypotheses? |
| A. | razor |
| B. | ockham razor |
| C. | learning element |
| D. | none of the mentioned |
| Answer» B. ockham razor | |
| 32. |
What will happen if the hypothesis space contains the true function? |
| A. | relizable |
| B. | unrelizable |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» B. unrelizable | |
| 33. |
What takes input as an object described bya set of attributes? |
| A. | tree |
| B. | graph |
| C. | decision graph |
| D. | decision tree |
| Answer» D. decision tree | |
| 34. |
What will happen if the hypothesis space contains the true function? |
| A. | realizable |
| B. | unrealizable |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» B. unrealizable | |
| 35. |
What takes input as an object described by a set of attributes? |
| A. | tree |
| B. | graph |
| C. | decision graph |
| D. | decision tree |
| Answer» D. decision tree | |
| 36. |
Factors which affect the performance of learner system does not include |
| A. | representation scheme used |
| B. | training scenario |
| C. | type of feedback |
| D. | good data structures |
| Answer» D. good data structures | |
| 37. |
Following is an example of active learning: |
| A. | news recommender system |
| B. | dust cleaning machine |
| C. | automated vehicle |
| D. | none of the mentioned |
| Answer» A. news recommender system | |
| 38. |
The process by which you become aware of messages through your sense is called |
| A. | organization |
| B. | sensation |
| C. | interpretation-evaluation |
| D. | perception |
| Answer» D. perception | |
| 39. |
Susan is so beautiful; I bet she is smart too. This is an example of |
| A. | the halo effect |
| B. | the primary effect |
| C. | a self-fulfilling prophecy |
| D. | the recency effect |
| Answer» A. the halo effect | |
| 40. |
_____ prevents you from seeing an individual as an individual rather than as a member of a group. |
| A. | cultural mores |
| B. | stereotypes |
| C. | schematas |
| D. | attributions |
| Answer» C. schematas | |
| 41. |
When you get fired from your job and you determine it is because your boss dislikes you, you are most likely exhibiting |
| A. | self-promotion |
| B. | fundamental attribution error |
| C. | over-attribution |
| D. | self-serving bias |
| Answer» D. self-serving bias | |
| 42. |
Mindless processing is |
| A. | careful, critical thinking |
| B. | inaccurate and faulty processing |
| C. | information processing that relies heavily on familiar schemata |
| D. | processing that focuses on unusual or novel events |
| Answer» C. information processing that relies heavily on familiar schemata | |
| 43. |
What kind of perception is used in printing? |
| A. | optical character recognition |
| B. | speech recognition |
| C. | perception |
| D. | none of the mentioned |
| Answer» A. optical character recognition | |
| 44. |
Selective retention occurs when |
| A. | we process, store, and retrieve information that we have already selected, organized, and interpreted |
| B. | we make choices to experience particular stimuli |
| C. | we make choices to avoid particular stimuli |
| D. | we focus on specific stimuli while ignoring other stimuli |
| Answer» A. we process, store, and retrieve information that we have already selected, organized, and interpreted | |
| 45. |
Which of the following strategies would NOT be effective at improving your communication competence? |
| A. | recognize the people, objects, and situations remain stable over time |
| B. | recognize that each person’s frame of perception is unique |
| C. | be active in perceiving |
| D. | distinguish facts from inference |
| Answer» A. recognize the people, objects, and situations remain stable over time | |
| 46. |
_____________ is measured by the number of mental structures we use, how abstract they are, and how elaborate they interact to shape our perceptions. |
| A. | intrapersonal structure |
| B. | perceptual set |
| C. | self-justification |
| D. | none of the above |
| Answer» D. none of the above | |
| 47. |
A perception check is |
| A. | a cognitive bias that makes us listen only to information we already agree with. |
| B. | a method teachers use to reward good listeners in the classroom. |
| C. | any factor that gets in the way of good listening and decreases our ability to interpret correctly. |
| D. | a response that allows you to state your interpretation and ask your partner whether or not that interpretation is correct. |
| Answer» D. a response that allows you to state your interpretation and ask your partner whether or not that interpretation is correct. | |
| 48. |
Which provides agents with information about the world they inhabit? |
| A. | sense |
| B. | perception |
| C. | reading |
| D. | hearing |
| Answer» B. perception | |
| 49. |
What is used to initiate the perception in the environment? |
| A. | sensor |
| B. | read |
| C. | actuators |
| D. | none of the mentioned |
| Answer» A. sensor | |
| 50. |
What is the study of light? |
| A. | biology |
| B. | lightology |
| C. | photometry |
| D. | all of the mentioned |
| Answer» C. photometry | |
| 51. |
How to increase the brightness of the pixel? |
| A. | sound |
| B. | amount of light |
| C. | surface |
| D. | waves |
| Answer» B. amount of light | |
| 52. |
How many kinds of reflection are available in image perception? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» B. 2 | |
| 53. |
What is meant by predicting the value of a state variable from the past? |
| A. | specular reflection |
| B. | diffuse reflection |
| C. | gaussian filter |
| D. | smoothing |
| Answer» D. smoothing | |
| 54. |
How many types of image processing techniques are there in image perception? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» C. 3 | |
| 55. |
Which is meant by assuming any two neighboring that are both edge pixels with consistent orientation? |
| A. | canny edge detection |
| B. | smoothing |
| C. | segmentation |
| D. | none of the mentioned |
| Answer» A. canny edge detection | |
| 56. |
What is the process of breaking an image into groups? |
| A. | edge detection |
| B. | smoothing |
| C. | segmentation |
| D. | none of the mentioned |
| Answer» C. segmentation | |
| 57. |
How many types of 3-D image processing techniques are there in image perception? |
| A. | 3 |
| B. | 4 |
| C. | 5 |
| D. | 6 |
| Answer» C. 5 | |
| 58. |
Which closely resembles propositional definite clause? |
| A. | resolution |
| B. | inference |
| C. | conjuction |
| D. | first-order definite clauses |
| Answer» D. first-order definite clauses | |
| 59. |
Which are more suitable normal form to be used with definite clause? |
| A. | positive literal |
| B. | negative literal |
| C. | generalized modus ponens |
| D. | neutral literal |
| Answer» C. generalized modus ponens | |
| 60. |
Which will be the instance of the class datalog knowledge bases? |
| A. | variables |
| B. | no function symbols |
| C. | first-order definite clauses |
| D. | none of the mentioned |
| Answer» B. no function symbols | |
| 61. |
Which knowledge base is called as fixed point? |
| A. | first-order definite clause are similar to propositional forward chaining |
| B. | first-order definite clause are mismatch to propositional forward chaining |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» A. first-order definite clause are similar to propositional forward chaining | |
| 62. |
Which algorithm will work backward from the goal to solve a problem? |
| A. | forward chaining |
| B. | backward chaining |
| C. | hill-climb algorithm |
| D. | none of the mentioned |
| Answer» B. backward chaining | |
| 63. |
Which is mainly used for automated reasoning? |
| A. | backward chaining |
| B. | forward chaining |
| C. | logic programming |
| D. | parallel programming |
| Answer» C. logic programming | |
| 64. |
What will backward chaining algorithm will return? |
| A. | additional statements |
| B. | substitutes matching the query |
| C. | logical statement |
| D. | all of the mentioned |
| Answer» B. substitutes matching the query | |
| 65. |
How can be the goal is thought of in backward chaining algorithm? |
| A. | queue |
| B. | list |
| C. | vector |
| D. | stack |
| Answer» D. stack | |
| 66. |
What are used in backward chaining algorithm? |
| A. | conjucts |
| B. | substitution |
| C. | composition of substitution |
| D. | none of the mentioned |
| Answer» C. composition of substitution | |
| 67. |
Which algorithm are in more similar to backward chainiing algorithm? |
| A. | depth-first search algorithm |
| B. | breadth-first search algorithm |
| C. | hill-climbing search algorithm |
| D. | all of the mentioned |
| Answer» A. depth-first search algorithm | |
| 68. |
Which problem can frequently occur in backward chaining algorithm? |
| A. | repeated states |
| B. | incompleteness |
| C. | complexity |
| D. | both a & b |
| Answer» D. both a & b | |
| 69. |
How the logic programming can be constructed? |
| A. | variables |
| B. | expressing knowledge in a formal language |
| C. | graph |
| D. | all of the mentioned |
| Answer» B. expressing knowledge in a formal language | |
| 70. |
What form of negation does the prolog allows? |
| A. | negation as failure |
| B. | proposition |
| C. | substitution |
| D. | negation as success |
| Answer» A. negation as failure | |
| 71. |
Which is omitted in prolog unification algorithm? |
| A. | variable check |
| B. | occur check |
| C. | proposition check |
| D. | both b & c |
| Answer» B. occur check | |
| 72. |
How many issues are available in describing degree of belief? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» B. 2 | |
| 73. |
What is used for probability theory sentences? |
| A. | conditional logic |
| B. | logic |
| C. | extension of propositional logic |
| D. | none of the mentioned |
| Answer» C. extension of propositional logic | |
| 74. |
Where does the degree of belief are applied? |
| A. | propositions |
| B. | literals |
| C. | variables |
| D. | statements |
| Answer» A. propositions | |
| 75. |
How many formal languages are used for stating propositions? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» B. 2 | |
| 76. |
What is the basic element for a language? |
| A. | literal |
| B. | variable |
| C. | random variable |
| D. | all of the mentioned |
| Answer» C. random variable | |
| 77. |
Which is the complete specification of the state of the world? |
| A. | atomic event |
| B. | complex event |
| C. | simple event |
| D. | none of the mentioned |
| Answer» A. atomic event | |
| 78. |
Which variable cannot be written in entire distribution as a table? |
| A. | discrete |
| B. | continuous |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» B. continuous | |
| 79. |
Which is created by using single propositional symbol? |
| A. | complex sentences |
| B. | atomic sentences |
| C. | composition sentences |
| D. | none of the mentioned |
| Answer» B. atomic sentences | |
| 80. |
Which is used to construct the complex sentences? |
| A. | symbols |
| B. | connectives |
| C. | logical connectives |
| D. | all of the mentioned |
| Answer» C. logical connectives | |
| 81. |
How many proposition symbols are there in artificial intelligence? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» B. 2 | |
| 82. |
How many logical connectives are there in artificial intelligence? |
| A. | 2 |
| B. | 3 |
| C. | 4 |
| D. | 5 |
| Answer» D. 5 | |
| 83. |
Which is used to compute the truth of any sentence? |
| A. | semantics of propositional logic |
| B. | alpha-beta pruning |
| C. | first-order logic |
| D. | both a & b |
| Answer» A. semantics of propositional logic | |
| 84. |
Which are needed to compute the logical inference algorithm? |
| A. | logical equivalence |
| B. | validity |
| C. | satisfiability |
| D. | all of the mentioned |
| Answer» D. all of the mentioned | |
| 85. |
From which rule does the modus ponens are derived? |
| A. | inference rule |
| B. | module rule |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» A. inference rule | |
| 86. |
Which is also called single inference rule? |
| A. | reference |
| B. | resolution |
| C. | reform |
| D. | none of the mentioned |
| Answer» B. resolution | |
| 87. |
Which form is called as conjunction of disjunction of literals? |
| A. | conjunctive normal form |
| B. | disjunctive normal form |
| C. | normal form |
| D. | all of the mentioned |
| Answer» A. conjunctive normal form | |
| 88. |
What can be viewed as single leteral of disjunction? |
| A. | multiple clause |
| B. | combine clause |
| C. | unit clause |
| D. | none of the mentioned |
| Answer» C. unit clause | |
| 89. |
Which is a refutation complete inference procedure for propositional logic? |
| A. | clauses |
| B. | variables |
| C. | propositional resolution |
| D. | proposition |
| Answer» C. propositional resolution | |
| 90. |
What kind of clauses is available in Conjunctive Normal Form? |
| A. | disjunction of literals |
| B. | disjunction of variables |
| C. | conjunction of literals |
| D. | conjunction of variables |
| Answer» A. disjunction of literals | |
| 91. |
What is the condition of literals in variables? |
| A. | existentially quantified |
| B. | universally quantified |
| C. | quantified |
| D. | none of the mentioned |
| Answer» B. universally quantified | |
| 92. |
Which can be converted to inferred equivalent CNF (Conjunction Normal Form) sentence? |
| A. | every sentence of propositional logic |
| B. | every sentence of inference |
| C. | every sentence of first-order logic |
| D. | all of the mentioned |
| Answer» C. every sentence of first-order logic | |
| 93. |
Which sentence will be unsatisfiable if the CNF (Conjunction Normal Form) sentence is unsatisfiable? |
| A. | search statement |
| B. | reading statement |
| C. | replaced statement |
| D. | original statement |
| Answer» D. original statement | |
| 94. |
Which rule is equal to resolution rule of first-order clauses? |
| A. | propositional resolution rule |
| B. | inference rule |
| C. | resolution rule |
| D. | none of the mentioned |
| Answer» A. propositional resolution rule | |
| 95. |
At which state does the propositional literals are complementary. |
| A. | if one variable is less |
| B. | if one is the negation of the other |
| C. | both a & b |
| D. | none of the mentioned |
| Answer» B. if one is the negation of the other | |
| 96. |
What is meant by factoring? |
| A. | removal of redundant variable |
| B. | removal of redundant literal |
| C. | addition of redundant literal |
| D. | addition of redundant variable |
| Answer» B. removal of redundant literal | |
| 97. |
What will happen if two literals are identical? |
| A. | remains the same |
| B. | added as three |
| C. | reduced to one |
| D. | none of the mentioned |
| Answer» C. reduced to one | |
| 98. |
When the resolution is called as refutation-complete? |
| A. | sentence is satisfiable |
| B. | sentence is unsatisfiable |
| C. | sentence remains the same |
| D. | none of the mentioned |
| Answer» B. sentence is unsatisfiable | |
| 99. |
Computers normally solve problem by breaking them down into a series of yes-or-no decisions represented by 1s and 0s. What is the name of the logic that allows computers to assign numerical values that fail somewhere between 0 and 1? |
| A. | human logic |
| B. | fuzzy logic |
| C. | boolean logic |
| D. | operational logic |
| Answer» B. fuzzy logic | |
| 100. |
The component of an ICAI (Intelligent Computer-Asslsted Instruction) presenting information to the student is the: |
| A. | student model |
| B. | problem-solving expertise |
| C. | tutoring module |
| D. | all of the mentioned |
| Answer» C. tutoring module | |
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